Index

A

• accelerated computing developer program
  • URL / Installing NVIDIA cuDNN v5.1+
• activation functions / Activation functions
• Adjusted Rand Index (ARI) / DNN performance analysis
• alternating least squares (ALS) / Model-based collaborative filtering
• Analog to Digital Converter (ADC) / Predictive models for clustering audio files
• Area Under Curve (AUC) / DNN performance analysis
• Artificial Neural Networks / Artificial Neural Networks
• attentional factorization machine (AFM) / Improved factorization machines for predictive analytics
• axon / Artificial Neural Networks

B

• Basic Linear Algebra Subroutines (BLAS) / Installing NVIDIA CUDA
• basic probability
  • for predictive modeling / Basic probability for predictive modeling
• Bayes' rule / Bayes' rule
• Bazel
  • URL, for installation / Installing TensorFlow from source
• bias neuron / Artificial Neural Networks
• biological neurons / Artificial Neural Networks
• BOW
  • used, for predictive analytics (PA) / Using BOW for predictive analytics
  • issues, defining / The problem definition
  • dataset description / The dataset description and exploration
  • dataset exploration / The dataset description and exploration
  • spam prediction, LR used with TensorFlow / Spam prediction using LR and BOW with TensorFlow
  • used, for spam prediction with TensorFlow / Spam prediction using LR and BOW with TensorFlow
• BRNN
  • about / BRNNs
  • used, for image classification / Using BRNN for image classification

C

• CBOW
  • used, for word embedding / Using CBOW for word embedding and model building
  • used, for model building / Using CBOW for word embedding and model building
  • model building / CBOW model building
  • reusing, for predicting sentiment / Reusing the CBOW for predicting sentiment
• central limit theorem (CLT)
  • about / Central limit theorem
  • skewness / Skewness and data distribution
  • data distribution / Skewness and data distribution
• Chi-square independence test / Chi-square independence test
• Chi-square test / Chi-square tests
• clustering / Unsupervised learning and clustering
• clustering audio files
  • predictive models / Predictive models for clustering audio files
• CNN-based predictive model
  • for sentiment analysis / CNN-based predictive model for sentiment analysis
  • movie and product review datasets, exploring / Exploring movie and product review datasets
  • CNN, used for predictive analytics (PA) about reviews / Using CNN for predictive analytics about movie reviews
• CNN hyperparameters
  • tuning / Tuning CNN hyperparameters
• CNN model
  • for emotion recognition / CNN model for emotion recognition
  • dataset description / Dataset description, Dataset description
  • architecture design / CNN architecture design
  • testing, on image / Testing the model on your own image
  • used, for predictive analytics (PA) / Using complex CNN for predictive analytics
• CNN predictive model
  • for image classification / CNN predictive model for image classification
• collaborative filtering (CF) / Factorization machines
• collaborative filtering approach
  • for movie recommendations / Collaborative filtering approach for movie recommendations
  • utility matrix / The utility matrix
  • dataset description / Dataset description
  • exploratory analysis, of dataset / Exploratory analysis of the dataset
• collaborative filtering approaches / Collaborative filtering approaches
• conditional entropy / Conditional entropy
• conditional probability
  • about / Conditional probability
  • chain rule / The chain rule of conditional probability
• content-based filtering approach / Content-based filtering approaches
• continuous bag-of-words / Continuous bag-of-words
• continuous skip-gram / Continuous skip-gram
• contrastive divergence / Restricted Boltzmann Machines
• conversion procedure
  • reference link / Dataset description
• convolutional neural networks (CNN)
  • about / CNNs and the drawbacks of regular DNNs
  • architecture / CNN architecture
• convolutional operations
  • about / Convolutional operations
  • applying, in TensorFlow / Applying convolution operations in TensorFlow
• convolutional operations, parameters
  • input / Applying convolution operations in TensorFlow
  • filter / Applying convolution operations in TensorFlow
  • strides / Applying convolution operations in TensorFlow
  • padding / Applying convolution operations in TensorFlow
  • use_cudnn_on_gpu / Applying convolution operations in TensorFlow
  • data_format / Applying convolution operations in TensorFlow
• Cornell University (CU) / Exploring movie and product review datasets
• correlation / Covariance and correlation
• covariance / Covariance and correlation
• CUDA toolkit
  • reference link / Installing NVIDIA CUDA
• cuDNN v5.1 library
  • URL, for downloading / Installing NVIDIA cuDNN v5.1+
• curse of dimensionality / Working principles of a predictive model

D

• data, in TensorFlow
  • reference link / Feeds and placeholders
• data distribution / Skewness and data distribution
• data model
  • in TensorFlow / Data model in TensorFlow
  • tensors / Tensors
  • rank / Rank
  • shape / Shape
  • data type / Data type
  • variables / Variables
  • fetches / Fetches
  • feeds / Feeds and placeholders
  • placeholders / Feeds and placeholders
• dataset
  • description / Description of the dataset
  • URL, for downloading / Description of the dataset
• dataset description
  • about / Dataset description, Dataset description
  • bank-additional-full.csv / Dataset description
  • bank-additional.csv / Dataset description
  • bank-full.csv / Dataset description
  • bank.csv / Dataset description
  • ratings data / Ratings data
  • movies data / Movies data
  • user data / User data
• datasets, for DSCI 425
  • reference link / Using K-means for predicting neighborhoods
• data type / Data type
• data value chain
  • for creating decisions / Data value chain for making decisions
• DBN
  • construction / Construction of a simple DBN
  • unsupervised pretraining / Unsupervised Pretraining
• Decision trees (DTs) / Ensemble method for survival prediction: random forest
• deep belief networks
  • about / Deep belief networks
  • Restricted Boltzmann Machines / Restricted Boltzmann Machines
  • DBN, construction / Construction of a simple DBN
  • used, for predictive analytics (PA) / Using deep belief networks for predictive analytics
  • reference link / Using deep belief networks for predictive analytics
• deep learning
  • for predictive analytics (PA) / Deep learning for better predictive analytics
• Deep Neural Networks
  • about / Deep Neural Networks
  • architecture / DNN architectures
  • performance analysis / DNN performance analysis
• dendrites / Artificial Neural Networks
• Distributed TensorFlow
  • reference link / Tuning CNN hyperparameters
• DNN
  • drawbacks / CNNs and the drawbacks of regular DNNs

E

• entropy
  • about / Entropy
  • Shannon entropy / Shannon entropy
  • joint entropy / Joint entropy
  • conditional entropy / Conditional entropy
• estimators
  • about / Transformers and estimators
• estimator transformer / Estimator transformer
• expectation / Expectation
• exploratory analysis
  • of dataset / Exploratory analysis of the dataset

F

• factorization machines
  • for recommendation systems / Factorization machines for recommendation systems
  • about / Factorization machines
  • problem definition / Problem definition and formulation
  • formulation / Problem definition and formulation
  • dataset description / Dataset description
  • preprocessing / Preprocessing
  • FM model, implementing / Implementing an FM model
  • improving, for predictive analytics (PA) / Improved factorization machines for predictive analytics
  • neural factorization machines / Neural factorization machines
• Fandango
  • reference link / Using linear regression for movie rating prediction
• Fast Fourier Transforms (FFT) / Installing NVIDIA CUDA
• feedforward neural network (FFNN) / DNN architectures
• fetches / Fetches
• fine-tuning DNN hyperparameters
  • about / Fine-tuning DNN hyperparameters
  • number of hidden layers / Number of hidden layers
  • number of neurons per hidden layer / Number of neurons per hidden layer
  • activation functions / Activation functions
  • weight and biases initialization / Weight and biases initialization
  • regularization / Regularization
• forwarding propagation step / Training an MLP

G

• Gated Recurrent Unit (GRU) / RNN architecture
• graphs / Vectors, matrices, and graphs
• GRU cell / GRU cell

H

• hidden layers / Multilayer perceptrons
• hybrid recommendation systems
  • collaborative filtering / Hybrid recommendation systems
  • content-based filtering / Hybrid recommendation systems
• hypothesis testing
  • about / Hypothesis testing
  • Chi-square test / Chi-square tests
  • Chi-square independence test / Chi-square independence test

I

• information gain / Information gain
• information theory
  • using, in predictive modeling / Using information theory in predictive modeling
  • using / Using information theory
  • using, in Python / Using information theory in Python
• input neurons / Artificial Neural Networks
• interquartile range (IQR) / Interquartile, range, and quartiles

J

• joint entropy / Joint entropy
• Jupyter Notebook
  • reference link / Data type

K

• K-means
  • used, for predictive analytics (PA) / Using K-means for predictive analytics
  • working / How K-means works
  • cluster assignment step / How K-means works
  • update step / How K-means works
  • used, for predicting neighborhoods / Using K-means for predicting neighborhoods
  • betweenness / Using K-means for predicting neighborhoods
  • withiness / Using K-means for predicting neighborhoods
  • totwithiness / Using K-means for predicting neighborhoods
• Kaggle
  • URL / From disaster to decision – Titanic survival example
• kNN
  • used, for predictive analytics (PA) / Using kNN for predictive analytics
  • working principles / Working principles of kNN
  • -based predictive model, implementing / Implementing a kNN-based predictive model

L

• Latent Drichilet Allocation (LDA) / Using text analytics
• latent factors (LFs) / Model-based collaborative filtering
• least squares fitting (LSF) / A bit of linear algebra
• libcupti-dev library
  • installing / Installing the libcupti-dev library
• linear algebra / Programming linear algebra
  • about / A bit of linear algebra
  • programming / Programming linear algebra
  • SciPy / Programming linear algebra
  • Linear algebra package (LAPACK) / Programming linear algebra
  • NumPy / Programming linear algebra
  • Pandas / Programming linear algebra
• linear independence / Span and linear independence
• linear regression
  • about / Getting started with TensorFlow – linear regression and beyond, Linear regression - revisited
  • source code / Source code for the linear regression
  • problem statement / Problem statement
  • used, for movie rating prediction / Using linear regression for movie rating prediction
• linear threshold unit (LTU) / Artificial Neural Networks
• log-likelihood function / Training an MLP
• Long Short-Term Memory (LSTM) / RNN architecture
• LSTM model evaluation / LSTM model evaluation
• LSTM model training / LSTM model training
• LSTM networks / LSTM networks
• LSTM predictive model / LSTM predictive model
  • for sentiment analysis / An LSTM predictive model for sentiment analysis
  • network design / Network design
  • LSTM model training / LSTM model training
  • TensorBoard, visualizing / Visualizing through TensorBoard
  • LSTM model evaluation / LSTM model evaluation

M

• 1m MovieLens dataset
  • reference link / Dataset description
• machine learning (ML) / Unsupervised learning and clustering
• marginal probability
  • about / Marginal probability
• Markov Chain Monte Carlo (MCMC) / Restricted Boltzmann Machines
• Markov Random Fields (MRF) / Deep belief networks
• mathematical explanation
  • reference link / Vectors
• matrices
  • about / Vectors, matrices, and graphs, Matrices
  • matrix addition / Matrix addition
  • multiplying / Multiplying two matrices
  • determinant of matrix, finding / Finding the determinant of a matrix
  • transpose of a matrix, finding / Finding the transpose of a matrix
  • simultaneous linear equations, solving / Solving simultaneous linear equations
  • eigenvectors / Eigenvalues and eigenvectors
  • eigenvalues / Eigenvalues and eigenvectors
• matrix factorization (FM) / Hybrid recommendation systems
• matrix subtraction / Matrix subtraction
• Mean Squared Error (MSE) / Implementing a kNN-based predictive model
• model-based collaborative filtering / Model-based collaborative filtering
• model evaluation / Model evaluation
• MovieLens data
  • reference link / Training the model with available ratings
• movie recommendation engine
  • implementing / Implementing a movie recommendation engine
  • model, training with ratings / Training the model with available ratings
  • saved model, inferencing / Inferencing the saved model
  • user-item table, generating / Generating a user-item table
  • movies, clustering / Clustering similar movies
  • movie rating prediction, by users / Movie rating prediction by users
  • top K movies, finding / Finding the top K movies
  • top K movies, predicting / Predicting top K similar movies
  • user-user similarity, computing / Computing the user-user similarity
  • recommendation system, evaluating / Evaluating the recommendation system
• movie review data
  • references / Exploring movie and product review datasets
• movie review dataset
  • reference link / Using CBOW for word embedding and model building
• multiarmed bandit's predictive model
  • developing / Developing a multiarmed bandit's predictive model
• multilayer perceptron
  • about / Multilayer perceptrons
  • MLP, training / Training an MLP
  • MLP, used / Using MLPs
  • preprocessing / Preprocessing
  • TensorFlow implementation, of MLP / A TensorFlow implementation of MLP
• multilayer perceptrons
  • used, for predictive analytics (PA) / Using multilayer perceptrons for predictive analytics
  • dataset description / Dataset description
• mutual information / Mutual information

N

• N-grams / N-gram
• network design
  • about / Network design
  • embedding layer / Network design
  • RNN layer / Network design
  • softmax or sigmoid layer / Network design
• neural factorization machine (NFM) / Improved factorization machines for predictive analytics
  • used, for movie recommendations / Using NFM for movie recommendations
  • model training / Model training
  • model evaluation / Model evaluation
• neural factorization machines
  • about / Neural factorization machines
  • dataset description / Dataset description
• Neural Networks (NNs) / Using kNN for predictive analytics
• NLP analytics pipelines
  • about / NLP analytics pipelines
  • text analytics, used / Using text analytics
• nonparametric model
  • versus parametric model / Parametric versus nonparametric model
• nonparametric predictive models / Nonparametric predictive models
• notation
  • in RL / Notation, policy, and utility in RL
• number of hidden layers / Number of hidden layers
• number of neurons per hidden layer / Number of neurons per hidden layer
• numerical linear algebra (NLA) / Programming linear algebra
• NVIDIA CUDA
  • installing / Installing NVIDIA CUDA
• NVIDIA cuDNN v5.1+
  • installing / Installing NVIDIA cuDNN v5.1+
• NVIDIA Graph Analytics Library (nvGRAPH) / Installing NVIDIA CUDA

P

• padding operations / Pooling layer and padding operations
  • subsampling operations, applying in TensorFlow / Applying subsampling operations in TensorFlow
• parametric model
  • versus nonparametric model / Parametric versus nonparametric model
• parametric predictive models / Parametric predictive models
• perceptron / Artificial Neural Networks
• placeholder
  • about / TensorFlow programming model
  • data type / TensorFlow programming model
  • shape / TensorFlow programming model
  • name / TensorFlow programming model
• policy
  • in RL / Notation, policy, and utility in RL, Policy
• pooling layer / Pooling layer and padding operations
• population / Population and sample
• predictive analytics (PA)
  • about / A basic introduction to predictive analytics, Why predictive analytics?
  • predictive model, working principles / Working principles of a predictive model
  • supervised learning / Supervised learning for predictive analytics
  • K-means, used / Using K-means for predictive analytics
  • kNN, used / Using kNN for predictive analytics
  • BOW, used / Using BOW for predictive analytics
  • deep learning / Deep learning for better predictive analytics
  • deep belief networks, used / Using deep belief networks for predictive analytics
  • Support Vector Machine (SVM) / CNN-based predictive model for sentiment analysis
  • Clustering with mean-NN classification / CNN-based predictive model for sentiment analysis
  • Naive Bayes / CNN-based predictive model for sentiment analysis
  • One-hot (sequence) vectors / CNN-based predictive model for sentiment analysis
  • N-grams / CNN-based predictive model for sentiment analysis
• predictive analytics tools
  • in Python / Predictive analytics tools in Python
• predictive model
  • developing, for time series data / Developing a predictive model for time series data
  • dataset, description / Description of the dataset
  • exploratory analysis / Preprocessing and exploratory analysis
  • preprocessing / Preprocessing and exploratory analysis
  • LSTM predictive model / LSTM predictive model
  • model evaluation / Model evaluation
• predictive modeling
  • information theory, using in / Using information theory in predictive modeling
• predictive models
  • for clustering audio files / Predictive models for clustering audio files
• principal component analysis / Principal component analysis
• probability / Probability and the random variables
• probability density functions (PDF)
  • about / Probability distributions
• probability distributions
  • about / Probability distributions
  • conditional independence / Independence and conditional independence
  • independence / Independence and conditional independence
• probability mass functions (PMF)
  • about / Probability distributions
• Python
  • installing / Installing and getting started with Python
  • obtaining / Installing and getting started with Python
  • reference link / Installing and getting started with Python
  • installing, on Windows / Installing on Windows
  • installing, on Linux / Installing Python on Linux
  • installing, on PIP / Installing and upgrading PIP (or PIP3)
  • upgrading, on PIP / Installing and upgrading PIP (or PIP3)
  • installing, on macOS / Installing Python on Mac OS
  • packages, installing / Installing packages in Python
  • about / Getting started with Python
  • data types / Python data types
  • string, used / Using strings in Python
  • list, used / Using lists in Python
  • tuple, used / Using tuples in Python
  • dictionary, used / Using dictionary in Python
  • set, used / Using sets in Python
  • functions / Functions in Python
  • classes / Classes in Python
  • predictive analytics tools / Predictive analytics tools in Python
  • information theory, using in / Using information theory in Python
• Python programming
  • reference link / Classes in Python

R

• random numbers
  • generating / Generating random numbers and setting the seed
• random sampling / Random sampling
• random variables / Probability and the random variables
• rank / Rank
• recommendation systems
  • about / Recommendation systems
  • collaborative filtering approaches / Collaborative filtering approaches
  • cold start / Collaborative filtering approaches
  • scalability / Collaborative filtering approaches
  • sparsity / Collaborative filtering approaches
  • content-based filtering approach / Content-based filtering approaches
  • hybrid recommendation systems / Hybrid recommendation systems
  • model-based collaborative filtering / Model-based collaborative filtering
  • factorization machines / Factorization machines for recommendation systems
  • in factorization machines / The cold start problem in recommendation systems
• rectified linear unit (ReLU) / DNN architectures
• regularization
  • about / Regularization
  • L2 regularization / Regularization
  • max-norm constraints / Regularization
  • dropout / Regularization
• reinforcement learning (RL)
  • about / Reinforcement learning
  • in predictive analytics / Reinforcement learning in predictive analytics
• requests package
  • reference link / Implementing a kNN-based predictive model
• Restricted Boltzmann Machines / Restricted Boltzmann Machines
• Restricted Boltzmann Machines (RBM) / Deep belief networks
• RNN
  • implementing, for spam prediction / Implementing an RNN for spam prediction
• RNN architecture
  • about / RNN architecture, Contextual information and the architecture of RNNs
  • contextual information / Contextual information and the architecture of RNNs
  • BRNN / BRNNs
  • LSTM networks / LSTM networks
  • GRU cell / GRU cell
• Root Mean Squared Error (RMSE) / DNN performance analysis
• R script
  • reference link / Using K-means for predicting neighborhoods

S

• sample
  • about / Population and sample
  • random sampling / Random sampling
  • expectation / Expectation
• sandard deviation (SD) / Standard deviation and variance
• seed
  • setting / Generating random numbers and setting the seed
• self-information
  • about / Self-information
  • mutual information / Mutual information
• sentiment analysis
  • CNN-based predictive model / CNN-based predictive model for sentiment analysis
  • LSTM predictive model / An LSTM predictive model for sentiment analysis
• Shannon entropy / Shannon entropy
• shape / Shape
• signals / Artificial Neural Networks
• singular value decomposition
  • about / Singular value decomposition
  • used, for data compression in predictive model / Data compression in a predictive model using SVD
• singular value decomposition (SVD) / Hybrid recommendation systems
• skewness / Skewness and data distribution
• Spam dataset
  • reference link / The dataset description and exploration
• span / Span and linear independence
• squared error function / Training an MLP
• Staked Auto-Encoders (SAEs) / DNN architectures
• standard deviation (SD) / Standard deviation and variance
• standard transformer / Standard transformer
• statistical model
  • about / Statistical models
  • nonparametric model, versus parametric model / Parametric versus nonparametric model
  • parametric predictive models / Parametric predictive models
  • nonparametric predictive models / Nonparametric predictive models
• statistics
  • used, in predictive modeling / Using statistics in predictive modeling
  • population / Population and sample
  • sample / Population and sample
  • central limit theorem (CLT) / Central limit theorem
  • sandard deviation (SD) / Standard deviation and variance
  • variance / Standard deviation and variance
  • interquartile range (IQR) / Interquartile, range, and quartiles
  • quartiles / Interquartile, range, and quartiles
  • hypothesis testing / Hypothesis testing
• stock price predictive model
  • developing / Developing a stock price predictive model
• StopWordsRemover / StopWordsRemover
• supervised learning
  • for predictive analytics (PA) / Supervised learning for predictive analytics
• Support Vector Machines (SVMs) / Using kNN for predictive analytics, Artificial Neural Networks
• synaptic terminals / Artificial Neural Networks

T

• telodendrian / Artificial Neural Networks
• TensorBoard
  • about / TensorBoard
  • working / How does TensorBoard work?
  • visualizing / Visualizing through TensorBoard
• TensorFlow
  • overview / General overview of TensorFlow
  • faster computing / General overview of TensorFlow
  • flexibility / General overview of TensorFlow
  • portability / General overview of TensorFlow
  • easy debugging / General overview of TensorFlow
  • unified API / General overview of TensorFlow
  • GPU computing, transparency / General overview of TensorFlow
  • easy use / General overview of TensorFlow
  • production scale / General overview of TensorFlow
  • extensibility / General overview of TensorFlow
  • supported / General overview of TensorFlow
  • wide adoption / General overview of TensorFlow
  • installing / Installing and configuring TensorFlow, Installing TensorFlow
  • configuring / Installing and configuring TensorFlow
  • installing, on Linux / Installing TensorFlow on Linux
  • with CPU support / Installing TensorFlow on Linux
  • with GPU support / Installing TensorFlow on Linux
  • Python, installing / Installing Python and nVidia driver
  • nVidia driver, installing / Installing Python and nVidia driver
  • installing, with native pip / Installing TensorFlow with native pip
  • installing, with virtualenv / Installing with virtualenv
  • installing, from source / Installing TensorFlow from source
  • installation, testing / Testing your TensorFlow installation
  • computational graph / TensorFlow computational graph
  • normal / TensorFlow computational graph
  • special / TensorFlow computational graph
  • tf.Operation objects / TensorFlow computational graph
  • tf.Tensor object / TensorFlow computational graph
  • variables / TensorFlow computational graph
  • tensors / TensorFlow computational graph
  • placeholders / TensorFlow computational graph
  • session / TensorFlow computational graph
  • programming model / TensorFlow programming model
  • data model / Data model in TensorFlow
  • Dataset API / Feeds and placeholders
  • feeding / Feeds and placeholders
  • reading, from files / Feeds and placeholders
  • preloaded data / Feeds and placeholders
  • convolutional operations, applying / Applying convolution operations in TensorFlow
• TensorFlow, on macOS
  • URL, for installation / Installing and configuring TensorFlow
• TensorFlow, on Windows
  • URL, for installation / Installing and configuring TensorFlow
• TensorFlow-based implementation of FM
  • reference link / Problem definition and formulation
• TensorFlow contrib
  • using / Using TensorFlow contrib
  • reference link / Using TensorFlow contrib
• TensorFlow Implementation, of Neural Factorization Machine
  • reference link / Using NFM for movie recommendations
• TensorFlow Python package
  • reference link / Installing TensorFlow with native pip
• tensors / Vectors, matrices, and graphs
  • about / Data model in TensorFlow, Tensors
  • reference link / Tensors
• text analytics
  • using / Using text analytics
  • sentiment analysis / Using text analytics
  • topic modelling / Using text analytics
  • term frequency - inverse document frequency (TF-IDF) / Using text analytics
  • named entity recognition / Using text analytics
  • event extraction / Using text analytics
• TF-IDF model
  • for predictive analytics (PA) / TF-IDF model for predictive analytics
  • TF, computing / How to compute TF, IDF, and TFIDF?
  • IDF, computing / How to compute TF, IDF, and TFIDF?
  • TFIDF, computing / How to compute TF, IDF, and TFIDF?
  • implementing, for spam prediction / Implementing a TF-IDF model for spam prediction
• tf.Tensor
  • reference link / TensorFlow programming model
• time series data
  • predictive model, developing / Developing a predictive model for time series data
• Titanic dataset
  • about / Taking decisions based on data - Titanic example
  • data value chain, for creating decisions / Data value chain for making decisions
  • example / From disaster to decision – Titanic survival example
• titanic dataset
  • about / From disaster to decision - Titanic example revisited
  • exploratory analysis / An exploratory analysis of the Titanic dataset
  • feature engineering / Feature engineering
  • logistic regression, for survival prediction / Logistic regression for survival prediction
  • TensorFlow contrib, using / Using TensorFlow contrib
  • linear SVM, for survival prediction / Linear SVM for survival prediction
  • ensemble method, for survival prediction / Ensemble method for survival prediction: random forest
  • comparative analysis / A comparative analysis
• transformers
  • about / Transformers and estimators
  • standard transformer / Standard transformer
  • estimator transformer / Estimator transformer
  • StopWordsRemover / StopWordsRemover
  • N-grams / N-gram

U

• UCI Machine Learning repository
  • reference link / Dataset description
• unsupervised learning / Unsupervised learning and clustering
• unsupervised pretraining / Unsupervised Pretraining
• utility
  • in RL / Notation, policy, and utility in RL, Utility
• utility matrix / The utility matrix

V

• variables / Variables
• variance / Standard deviation and variance
• vectors / Vectors, matrices, and graphs, Vectors

W

• weight and biases initialization
  • about / Weight and biases initialization
  • zero initialization, avoiding / Weight and biases initialization
  • small random numbers / Weight and biases initialization
  • biases, initializing / Weight and biases initialization
• within-cluster sums of squares (WCSS) / DNN performance analysis
• Word2vec
  • used, for sentiment analysis / Using Word2vec for sentiment analysis
  • continuous bag-of-words / Continuous bag-of-words
  • continuous skip-gram / Continuous skip-gram
  • CBOW, used, for word embedding / Using CBOW for word embedding and model building
  • CBOW, used, for model building / Using CBOW for word embedding and model building